Methods and apparatus for colorimetrically characterizing color deviation in color imaging devices

ABSTRACT

Methods and apparatus are provided that print a set of similar color patches using a color output device, determine colorimetric values for each color patch, calculate a standard deviation value for each of the colorimetric values, and calculate a numerical value that is a function of the standard deviation values and that represents the colorimetric deviation of the set.

FIELD OF THE INVENTION

This invention relates to color imaging devices. More particularly, thisinvention pertains to methods and apparatus for calorimetricallycharacterizing color deviation in color imaging devices.

BACKGROUND

Color imaging devices, such as color copiers, color printers, colorprinting presses and other similar color imaging devices often exhibitvisible color variations in their printed output. These visiblevariations may occur within a single page (spatial page variations), andalso from page to page (page-to-page variations), even though the inputto the imaging device is identical for each page. This phenomenon isoften referred to as “engine drift,” or “engine instability,” and mayresult from variations in manufacturing tolerances or aging of theimaging device, time-dependent variations in the colorants used to formthe image (e.g., toner or ink), and variations in the ambientenvironment of the device.

Although engine drift and engine instability have been recognized forsome time, there has not been an objective technique to characterizethis phenomenon that correlates well with the visually perceivedphenomenon. Indeed, previously known techniques for characterizingengine drift have often been based on density measurements. In one suchpreviously known technique, a color imaging device is used to print atest pattern that includes test patches, which are then measured using adensitometer or other similar device that provides measurements ofreflected density. After obtaining a large number of measurements (i.e.,from multiple locations within a page and from multiple pages) themeasured density values are averaged, and a standard deviationmeasurement is calculated from the measured data.

Although such previously known density-based techniques may be used tocharacterize engine drift, the techniques are not useful indicators ofhuman perception of engine drift. In particular, density measurements donot correlate well with human perception of color, and density-basedindicators of color variation do not correlate well with humanperception of color differences. As a result, previously knowndensity-based techniques for characterizing engine drift do not bear aclose relationship to how a human observer would perceive such enginedrift.

It therefore would be desirable to provide methods and apparatus forobjectively characterizing color variations in a color output device.

It further would be desirable to provide methods and apparatus forcharacterizing color variations in a color output device in a mannerthat corresponds to human perception of such variations.

SUMMARY

In view of the foregoing, it is an object of this invention to providemethods and apparatus for objectively characterizing color variations ina color output device.

It further is an object of this invention to provide methods andapparatus for characterizing color variations in a color output devicein a manner that corresponds to human perception of such variations.

These and other objects of this invention are accomplished by providingmethods and apparatus that print a set of similar color patches using acolor output device, determine colorimetric values for each color patch,calculate a standard deviation value for each of the calorimetricvalues, and calculate a numerical value that is a function of thestandard deviation values and that represents the colorimetric deviationof the set.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-mentioned objects and features of the present invention can bemore clearly understood from the following detailed descriptionconsidered in conjunction with the following drawings, in which the samereference numerals denote the same elements throughout, and in which:

FIG. 1 is a block diagram of an exemplary system in accordance with thisinvention;

FIG. 2 is a flowchart of an exemplary method in accordance with thisinvention;

FIG. 3 is a diagram of an exemplary test pattern for use with systemsand methods in accordance with this invention;

FIG. 4 is a diagram of exemplary output pages for use with systems andmethods in accordance with this invention; and

FIG. 5 is a diagram of an alternative exemplary test pattern for usewith systems and methods in accordance with this invention.

DETAILED DESCRIPTION

Referring to FIG. 1, an exemplary system for characterizing colorvariations in a color output device in accordance with this invention isdescribed. System 10 includes image source 12, color imaging device 14,output pages 16, measurement device 22 and processor 24. Image source 12includes image file 26, which includes digital data representing a testpattern 18 to be printed by color imaging device 14. Image source 12 maybe a personal computer, laptop computer, handheld computer, computerworkstation, print server, personal digital assistant, or any othersimilar device that may be used to provide image files for printing bycolor imaging devices.

Image source 12 may include a software application (not shown) used togenerate image file 26. For example, image source 12 may be a personalcomputer that includes Adobe PageMaker software that may be used togenerate image file 26. Image file 26 may be a digital data file thatdescribes test pattern 18 in a page description language, such asPostScript, PCL, or other similar page description language, or maysimply be a raster image, such as a TIFF image, RAW image, or othersimilar raster image. Color imaging device 14 may be a color printer,color copier, printing press, or other similar color imaging device thatuses one or more colorants to provide output pages 16 including testpattern 18. For example, color imaging device 14 may be a color printerthat uses cyan (“C”), magenta (“M”), yellow (“Y”) and black (“K”)colorants. Test pattern 18 includes one or more color patches 20.

Measurement device 22 may be any conventional measurement device thatmay be used to provide colorimetric data that describes a printedsample, such as a calorimeter, spectrophotometer, spectrocolorimeter, orother similar device. For example, measurement device 22 may be aSpectrolino spectrophotometer manufactured by GretagMacbeth LLC, NewWindsor, N.Y. Measurement device 22 provides colorimetric data, such asCIE LAB data (referred to herein as “LAB data”) CIE XYZ data (referredto herein as “XYZ data”), CIE LUV data (referred to herein as “LUVdata”), CIE LCH data (referred to herein as “LCH data”), or othersimilar calorimetric data that describes printed samples, such as colorpatches 20. Processor 24 may be a personal computer, laptop computer,handheld computer, computer workstation, print server, personal digitalassistant, or any other similar device that may be used to receivecolorimetric data, such as LAB data (i.e., L, a and b values), LUV data(i.e., L, u and v values), LCH data (i.e., L, C and H values), or othersimilar colorimetric data from measurement device 22 and generatetherefrom numerical values that characterize color variations in a coloroutput device in accordance with this invention. Persons of ordinaryskill in the art will understand that the functions of processor 24 maybe implemented by image source 12.

Referring now to FIGS. 1 and 2, an exemplary method 30 forcharacterizing color variations in a color output device in accordancewith this invention is described. At step 32, color imaging device 14 isused to print test pattern 18 of one or more color patches 20 on one ormore output pages 16. For example, a user of image source 12 may issue aprint command to print multiple copies of image file 26 on color imagingdevice 14. At step 34, calorimetric values are determined for each colorpatch 20. For example, measurement device 22 may be used to determineLAB data for each color patch 20 on each of the output pages 16.

Referring now to FIG. 3, an exemplary output page 16 including exemplarytest pattern 18 is described. Test pattern 18 includes one or moreregions 40, with each region including one or more color patches 20. Asshown in FIG. 3, test pattern 18 includes four regions 40 a-40 d, witheach region including fifteen color patches 20. Persons of ordinaryskill in the art will understand that test pattern 18 may include moreor less than four regions 40, and each region may include more or lessthan 15 color patches 20. Each color patch 20 is comprised of acorresponding specified percentage of the colorants used by colorimaging device 14 (e.g., C, M, Y and K). For example, color patches 20aa, 20 ab, 20 ac and 20 ad may be comprised of 100% C and 0% M, 0% Y and0% K, color patches 20 ba, 20 bb, 20 bc and 20 bd may be comprised of 0%C, 100% M, 0% Y and 100% K, and color patches 20 ca, 20 cb, 20 cc and 20cd may be comprised of 100% C, 0% M, 100% Y and 100% K. Persons ofordinary skill in the art will understand that other percentages ofcolorants also may be used for color patches 20.

Each output page 16 may include multiple similar color patches 20distributed throughout test pattern 18. For example, color patches 20aa, 20 ab, 20 ac and 20 ad in regions 40 a-40 d are similar to oneanother, and are appear at four different locations throughout testpattern 18. Similarly, color patches 20 ba, 20 bb, 20 bc and 20 bd inregions 40 a-40 d are similar to one another, and are appear at fourdifferent locations throughout test pattern 18, and color patches 20 ca,20 cb, 20 cc and 20 cd in regions 40 a-40 d are similar to one another,and are appear at four different locations throughout test pattern 18.Persons of ordinary skill in the art will understand that the specificlocation of each color patch 20 is not important, but similar colorpatches preferably should be distributed over various parts of outputpage 16. As described below, similar color patches 20 located within anindividual output page 16 are used to determine spatial page colorvariations.

Referring now to FIG. 4, an exemplary set of output pages 16 aredescribed. Each of output pages 16 ₁-16 ₅ includes test pattern 18including color patches 20. In particular, output page 16 ₁ includescolor patches 20 aa ₁ and 20 ba ₁, output page 162 includes colorpatches 20 aa ₂ and 20 ba ₂, output page 163 includes color patches 20aa ₃ and 20 ba ₃, output page 164 includes color patches 20 aa ₄ and 20ba ₄, and output page 16 ₅ includes color patches 20 aa ₅ and 20 ba ₅.Across the five output pages 16 ₁-16 ₅, color patches 20 aa ₁, 20 aa ₂,20 aa ₃, 20 aa ₄ and 20 aa ₅ are similar to one another, and colorpatches 20 ba ₁, 20 ba ₂, 20 ba ₃, 20 ba ₄ and 20 ba ₅ are similar toone another. As described below, similar color patches 20 aa ₁-20 aa ₅and 20 ba ₁-20 ba ₅ located across multiple output pages 16 ₁-16 ₅ areused to determine page-to-page color variations.

Referring again to FIGS. 1 and 2, at step 34, colorimetric values aredetermined for each color patch 20 on each output page 16. Thus, forexample, measurement device 22 may be used to determine LAB data foreach color patch 20 on each of output pages 16 ₁-16 ₅ illustrated inFIGS. 3 and 4, for a total of three hundred sets of LAB data (i.e.,sixty color patches 20 on each of the five output pages 16 ₁-16 ₅). Atstep 36, the standard deviation of each of the L, a and b values may bedetermined for sets of similar color patches 20. In particular, for eachoutput page 16, the spatial standard deviation σL_(spatial),σ_(spatial), and σb_(spatial), of each of the L, a and b values,respectively, may be determined for similar color patches 20 within thepage: $\begin{matrix}{{\sigma\quad L_{spatial}} = \sqrt{\frac{\sum\limits^{\quad}\quad\left( {L - \overset{\_}{L}} \right)^{2}}{N - 1}}} & (1) \\{{\sigma\quad a_{spatial}} = \sqrt{\frac{\sum\limits^{\quad}\quad\left( {a - \overset{\_}{a}} \right)^{2}}{N - 1}}} & (2) \\{{\sigma\quad b_{spatial}} = \sqrt{\frac{\sum\quad\left( {b - \overset{\_}{b}} \right)^{2}}{N - 1}}} & (3)\end{matrix}$where {overscore (L)}, {overscore (a)} and {overscore (b)} are theaverage L, a and b values, respectively, in each set, and N is thenumber of samples per set. For example, referring again to FIG. 3, thespatial standard deviation of each of the L, a and b values may bedetermined for the set of color patches 20 aa-20 ad, the set of colorpatches 20 ba-20 bd, the set of color patches 20 ca-20 cd, and so on.

In addition, for output pages 16 ₁-16 ₅, the page-to-page standarddeviation (σL_(P-P), σa_(P-P), and σb_(P-P), of each of the L, a and bvalues, respectively, may be determined for similar color patches 20across the pages: $\begin{matrix}{{\sigma\quad L_{P - P}} = \sqrt{\frac{\sum\limits^{\quad}\quad\left( {L - \overset{\_}{L}} \right)^{2}}{N - 1}}} & (4) \\{{\sigma\quad a_{P - P}} = \sqrt{\frac{\sum\limits^{\quad}\quad\left( {a - \overset{\_}{a}} \right)^{2}}{N - 1}}} & (5) \\{{\sigma\quad b_{P - P}} = \sqrt{\frac{\sum\limits^{\quad}\quad\left( {b - \overset{\_}{b}} \right)^{2}}{N - 1}}} & (6)\end{matrix}$where {overscore (L)}, {overscore (a)} and {overscore (b)} are theaverage L, a and b values, respectively, in each set, and N is thenumber of samples per set. For example, referring again to FIG. 4, thepage-to-page standard deviation of each of the L, a and b values may bedetermined for the set of color patches 20 aa ₁-20 aa ₅, the set ofcolor patches 20 ba ₁-20 ba ₅, and so on.

Referring again to FIG. 2, at step 38, a numerical value is calculatedthat is a function of the standard deviation values and that representsa colorimetric characterization of the color deviation of a set ofsimilar color values. For example, a formula based on Euclidean distancecalculations may be used. In particular, for each output page 16, anumerical value E_(spatial) that represents the spatial color deviationwithin the page may be determined for similar color patches 20 based onthe corresponding standard deviation values calculated in step 36:E _(spatial)={square root}{square root over (σL _(spatial) ² +σa_(spatial) ² +σb _(spatial) ²)}  (7)In addition, for output pages 16 ₁-16 ₅, a numerical value E_(P-P) thatrepresents the page-to-page color deviation across the pages may bedetermined for similar color patches 20 across the pages based on thecorresponding standard deviation values calculated in step 36:E _(P-P)={square root}{square root over (L _(P-P) ² σa _(P-P) ² +σb_(P-P) ²)}  (8)

The following table illustrates exemplary E_(spatial) and E_(P-P) valuesfor four color patches 20 a per output page 16, across five output pages16 ₁-16 ₅, where each sample patch 20 a has colorant values 100% C, 0%M, 0% Y and 0% K: Page Sample L a b 1 20aa₁ 47.53 −31.07 −55.4 1 20ab₁48.12 −31.09 −55.3 1 20ac₁ 44.17 −27.24 −57.09 1 20ad₁ 47.72 −31.19−55.33 2 20aa₂ 47.72 −30.5 −54.84 2 20ab₂ 47.56 −30.58 −55.37 2 20ac₂43.17 −25.81 −57.34 2 20ad₂ 45.98 −29.76 −55.97 3 20aa₃ 46.95 −29.95−55.35 3 20ab₃ 47.41 −29.8 −55.49 3 20ac₃ 43.57 −25.67 −57.11 3 20ad₃45.24 −28.43 −56.51 4 20aa₄ 48.21 −30.2 −55.13 4 20ab₄ 48.31 −29.73−55.32 4 20ac₄ 44.38 −25.8 −56.86 4 20ad₄ 46.82 −29.12 −55.82 5 20aa₅47.19 −32.07 −55.07 5 20ab₅ 48.02 −32.27 −55.28 5 20ac₅ 43.66 −27.39−57.24 5 20ad₅ 45.03 −29.38 −56.64

Based on these exemplary values, and using equations (1)-(3) and (7),the or deviation E_(spatial) for each page is: Page E_(spatial) 12.971285 2 3.24904 3 2.735232 4 2.808842 5 3.169704

In addition, using equations (4)-(6) and (8), the page-to-page colordeviation ch patch location is: Patch Location E_(P—P) a 1.00057 b1.117555 c 0.999795 d 1.609404

Persons of ordinary skill in the art will understand that the spatialcolor E_(spatial) values determined across multiple pages may beaveraged to provide spatial color deviation, and that the page-to-pagecolor deviation E_(P-P) values determined across multiple patchlocations may be averaged to provide an average page-to-page colordeviation. Persons of ordinary skill in the art also will understandthat other functions of may be used to characterize the color deviationof a set of similar color values. For example, functions based on CMCcolor deviation formulae or other similar color difference formulae maybe used.

In addition, persons of ordinary skill in the art will understand thattest pattern 18 may include more than four regions 40 a-40 d, and eachregion may include more than fifteen color patches 20. For example, FIG.5 illustrates an alternative exemplary test pattern 18 that eachincludes four regions 40 a-40 d, with each region including sixty colorpatches P1-P60. Exemplary colorant values (specified in percent) foreach patch are illustrated in the following table: Patch C M Y K P1 25 00 0 P2 50 0 0 0 P3 75 0 0 0 P4 100 0 0 0 P5 0 25 0 25 P6 0 50 0 50 P7 075 0 75 P8 0 100 0 100 P9 25 25 25 0 P10 50 50 50 0 P11 75 75 75 0 P12100 100 100 0 P13 0 0 25 25 P14 0 0 50 50 P15 0 0 75 75 P16 0 0 100 100P17 0 25 0 0 P18 0 50 0 0 P19 0 75 0 0 P20 0 100 0 0 P21 25 25 0 25 P2250 50 0 50 P23 75 75 0 75 P24 100 100 0 100 P25 25 0 25 25 P26 50 0 5050 P27 75 0 75 75 P28 100 0 100 100 P29 0 0 25 0 P30 0 0 50 0 P31 0 0 750 P32 0 0 100 0 P33 0 0 0 25 P34 0 0 0 50 P35 0 0 0 75 P36 0 0 0 100 P370 25 25 0 P38 0 50 50 0 P39 0 75 75 0 P40 0 100 100 0 P41 25 0 25 0 P4250 0 50 0 P43 75 0 75 0 P44 100 0 100 0 P45 25 25 25 25 P46 50 50 50 50P47 75 75 75 75 P48 100 100 100 100 P49 25 0 0 25 P50 50 0 0 50 P51 75 00 75 P52 100 0 0 100 P53 25 25 0 0 P54 50 50 0 0 P55 75 75 0 0 P56 100100 0 0 P57 0 25 25 25 P58 0 50 50 50 P59 0 75 75 75 P60 0 100 100 100As previously mentioned, persons of ordinary skill in the art willunderstand that the specific location of each color patch 20 is notimportant, but similar color patches preferably should be distributedover various parts of output page 16.

The foregoing merely illustrates the principles of this invention, andvarious modifications can be made by persons of ordinary skill in theart without departing from the scope and spirit of this invention.

1. A method for characterizing color variations in a color outputdevice, the method comprising: printing a set of similar color patchesusing the color output device; determining colorimetric values for eachcolor patch; calculating a standard deviation value for each of thecolorimetric values; and calculating a numerical value that is afunction of the standard deviation values and that represents acalorimetric characterization of color deviation of the set.
 2. Themethod of claim 1, wherein the color output device is a color printer.3. The method of claim 1, wherein the color output device is a colorcopier.
 4. The method of claim 1, wherein the color output device is acolor printing press.
 5. The method of claim 1, wherein the colorpatches comprise predetermined percentages of cyan, magenta, yellow andblack colorants.
 6. The method of claim 1, wherein printing furthercomprises printing the set of similar color patches on an output page.7. The method of claim 1, wherein printing further comprises printingthe set of similar color patches on a plurality of output pages.
 8. Themethod of claim 1, wherein the colorimetric values comprise L, a and bvalues.
 9. The method of claim 1, wherein the colorimetric valuescomprise X, Y and Z values.
 10. The method of claim 1, whereindetermining further comprises measuring the color patches using acolorimeter.
 11. The method of claim 1, wherein determining furthercomprises measuring the color patches using a spectrophotometer.
 12. Themethod of claim 1, wherein determining further comprises measuring thecolor patches using a spectrocolorimeter.
 13. The method of claim 1,wherein the function comprises a sum of squares of the standarddeviation values.
 14. The method of claim 1, wherein the functioncomprises a square root of a sum of squares of the standard deviationvalues.
 15. A system for characterizing color variations in a coloroutput device, the system comprising: an image file comprising a set ofsimilar color patches, the image file adapted to be printed on the coloroutput device; a measurement device for determining calorimetric valuesfor each printed color patch; and a processor adapted to calculate astandard deviation value for each of the colorimetric values, and anumerical value that is a function of the standard deviation values andthat represents a calorimetric characterization of color deviation ofthe set.
 16. The system of claim 15, wherein the color output device isa color printer.
 17. The system of claim 15, wherein the color outputdevice is a color copier.
 18. The system of claim 15, wherein the coloroutput device is a color printing press.
 19. The system of claim 15,wherein the color patches comprise predetermined percentages of cyan,magenta, yellow and black colorants.
 20. The system of claim 15, whereinthe set of similar color patches are printed on an output page.
 21. Thesystem of claim 15, wherein the set of similar color patches are printedon a plurality of output pages.
 22. The system of claim 15, wherein thecolorimetric values comprise L, a and b values.
 23. The system of claim15, wherein the colorimetric values comprise X, Y and Z values.
 24. Thesystem of claim 15, wherein the measuring device comprises acolorimeter.
 25. The system of claim 15, wherein the measuring devicecomprises a spectrophotometer.
 26. The system of claim 15, wherein themeasuring device comprises a spectrocolorimeter.
 27. The system of claim15, wherein the function comprises a sum of squares of the standarddeviation values.
 28. The system of claim 15, wherein the functioncomprises a square root of a sum of squares of the standard deviationvalues.